Binary Inference for Primary User Separation in Cognitive Radio Networks

TL;DR

This paper introduces a novel binary inference algorithm for primary user separation in cognitive radio networks, enabling accurate detection and characterization of PUs from boolean OR mixture observations without prior activity knowledge.

Contribution

The paper proposes a new binary inference method for PU separation that outperforms traditional models and reveals PUs' transmission details in collaborative spectrum sensing.

Findings

High inference accuracy achieved in simulations

Effective separation of PUs without prior activity knowledge

Reveals PUs' transmission statistics and activities

Abstract

Spectrum sensing receives much attention recently in the cognitive radio (CR) network research, i.e., secondary users (SUs) constantly monitor channel condition to detect the presence of the primary users (PUs). In this paper, we go beyond spectrum sensing and introduce the PU separation problem, which concerns with the issues of distinguishing and characterizing PUs in the context of collaborative spectrum sensing and monitor selection. The observations of monitors are modeled as boolean OR mixtures of underlying binary sources for PUs. We first justify the use of the binary OR mixture model as opposed to the traditional linear mixture model through simulation studies. Then we devise a novel binary inference algorithm for PU separation. Not only PU-SU relationship are revealed, but PUs' transmission statistics and activities at each time slot can also be inferred. Simulation results show that without any prior knowledge regarding PUs' activities, the algorithm achieves high inference accuracy even in the presence of noisy measurements.